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Object Tracking Using Genetic Evolution Based Kernel Particle Filter

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 4040))

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Abstract

A new particle filter, which combines genetic evolution and kernel density estimation, is proposed for moving object tracking. Particle filter (PF) solves non-linear and non-Gaussian state estimation problems in Monte Carlo simulation using importance sampling. Kernel particle filter (KPF) improves the performance of PF by using density estimation of broader kernel. However, it has the problem which is similar to the impoverishment phenomenon of PF. To deal with this problem, genetic evolution is introduced to form new filter. Genetic operators can ameliorate the diversity of particles. At the same time, genetic iteration drives particles toward their close local maximum of the posterior probability. Simulation results show the performance of the proposed approach is superior to that of PF and KPF.

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Author information

Authors and Affiliations

  1. Departmant of Information and Electronics Engineering, Zhejiang University, Hangzhou, Zhejiang, 310013, China

    Qicong Wang & Jilin Liu

  2. College of Information & Electronics Engineering, Taizhou University, Linhai, Zhejiang, 317000, China

    Zhigang Wu

Authors
  1. Qicong Wang

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  2. Jilin Liu

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  3. Zhigang Wu

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Editor information

Editors and Affiliations

  1. Institute for Transport Research, German-Aerospace Center (DLR), Rutherfordstr. 2, 12489, Berlin, Germany

    Ralf Reulke

  2. Fachbereich Mathematik, Universität Hamburg, Bundesstr. 55, 20146, Hamburg

    Ulrich Eckardt

  3. Dresden University of Technology,  

    Boris Flach

  4. Institut für Informatik, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Uwe Knauer

  5. FU Berlin, Arnimallee 3, 14195, Berlin, Germany

    Konrad Polthier

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, Q., Liu, J., Wu, Z. (2006). Object Tracking Using Genetic Evolution Based Kernel Particle Filter. In: Reulke, R., Eckardt, U., Flach, B., Knauer, U., Polthier, K. (eds) Combinatorial Image Analysis. IWCIA 2006. Lecture Notes in Computer Science, vol 4040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774938_37

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